
"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)



class 			lowercase					( unittest.TestCase		):






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Any      ,SCREAMING_SNAKE_CASE_  :	Tuple=13      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=7      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=True      ,SCREAMING_SNAKE_CASE_  :	List[str]=True      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=99      ,SCREAMING_SNAKE_CASE_  :	str=32      ,SCREAMING_SNAKE_CASE_  :	List[str]=5      ,SCREAMING_SNAKE_CASE_  :	List[Any]=4      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=37      ,SCREAMING_SNAKE_CASE_  :	int="gelu"      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	int=512      ,SCREAMING_SNAKE_CASE_  :	int=16      ,SCREAMING_SNAKE_CASE_  :	Dict=2      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.02      ,SCREAMING_SNAKE_CASE_  :	Tuple=4      ,)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  parent
				lowerCAmelCase												=  batch_size
				lowerCAmelCase												=  seq_length
				lowerCAmelCase												=  is_training
				lowerCAmelCase												=  use_attention_mask
				lowerCAmelCase												=  use_token_type_ids
				lowerCAmelCase												=  use_labels
				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  hidden_size
				lowerCAmelCase												=  num_hidden_layers
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  intermediate_size
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  max_position_embeddings
				lowerCAmelCase												=  type_vocab_size
				lowerCAmelCase												=  type_sequence_label_size
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  num_choices






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size			)

				lowerCAmelCase												=  None
				if self.use_attention_mask:
					lowerCAmelCase												=  random_attention_mask([self.batch_size, self.seq_length]			)

				lowerCAmelCase												=  None
				if self.use_token_type_ids:
					lowerCAmelCase												=  ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size			)

				lowerCAmelCase												=  RobertaPreLayerNormConfig(
				    vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,is_decoder=SCREAMING_SNAKE_CASE_      ,initializer_range=self.initializer_range      ,)

				return config, input_ids, token_type_ids, attention_mask






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.prepare_config_and_inputs()
				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  config_and_inputs
				lowerCAmelCase												=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict






			def 					UpperCAmelCase							(self  :	str			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.prepare_config_and_inputs()
				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  config_and_inputs

				lowerCAmelCase												=  True
				lowerCAmelCase												=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
				lowerCAmelCase												=  ids_tensor([self.batch_size, self.seq_length]      ,vocab_size=2			)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     True

			lowercase        =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  FlaxRobertaPreLayerNormModelTester(self			)






			@slow
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	List[Any]:





				"""simple docstring"""


				for model_class_name in self.all_model_classes:
					lowerCAmelCase												=  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      ,from_pt=SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  model(np.ones((1, 1)			)			)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





@require_flax
class 			lowercase					( unittest.TestCase		):






			@slow
			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      ,from_pt=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,dtype=jnp.intaa			)
				lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			)[0]
				lowerCAmelCase												=  [1, 11, 50_265]
				self.assertEqual(list(output.shape			)      ,SCREAMING_SNAKE_CASE_			)
				# compare the actual values for a slice.
				lowerCAmelCase												=  np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]      ,dtype=np.floataa			)
				self.assertTrue(np.allclose(output[:, :3, :3]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-4			)			)






			@slow
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''      ,from_pt=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      ,dtype=jnp.intaa			)
				lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			)[0]
				# compare the actual values for a slice.
				lowerCAmelCase												=  np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]      ,dtype=np.floataa			)
				self.assertTrue(np.allclose(output[:, :3, :3]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-4			)			)




"""simple docstring"""






class 			lowercase					:






			def __init__(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {}






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	None:





				"""simple docstring"""


				print(self.vertex			)
				for i in self.vertex:
					print(SCREAMING_SNAKE_CASE_      ,''' -> '''      ,''' -> '''.join([str(SCREAMING_SNAKE_CASE_			) for j in self.vertex[i]]			)			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	None:





				"""simple docstring"""


				if from_vertex in self.vertex:
					self.vertex[from_vertex].append(SCREAMING_SNAKE_CASE_			)
				else:
					# else make a new vertex
					lowerCAmelCase												=  [to_vertex]






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [False] * len(self.vertex			)

				# call the recursive helper function
				for i in range(len(self.vertex			)			):
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	list			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  True

				print(SCREAMING_SNAKE_CASE_      ,end=''' '''			)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
					UpperCAmelCase        =				Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("""DFS:""")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3


